whatmodel.sh / image / Ideogram 3.0
Ideogram 3.0
↗ share
+ contribute
Ideogram 3.0 is a closed-weights image model from Ideogram, released March 2025. Licence: proprietary.
Score within the LMArena text-to-image comparison set only.
$ how it compares
The image models ranked either side of Ideogram 3.0, out of 197 with a score, then more from Ideogram.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 92 | 53±4 | api $0.030/image | |||
| 93 | 53±5 | api $0.0035/image | |||
| 94 | 52±2 | api $0.060/image | |||
| 95 | 52±3 | comparison only | |||
| 96 | 52±2 | comparison only | |||
| more from Ideogram | |||||
| 19 | 68±3 | comparison only | |||
| 23 | 66±2 | api $0.060/image +local | |||
| 118 | 46±2 | api $0.080/image | |||
$ what it's good at
text to image▂▅▅▃▄▄█▃▁▂▁▁ #64 of 126 * · 82% of its score · 2 tests
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena text-to-image | 1048.4 ✎ | ▂▂▃▄▇█▄▂▄▂·▂ beats 15/75 | 62% |
| ServImage task acceptance (human payment decisions, commercial design tasks) | 25.9% ✎ | ▂▃··▂··▃█··▂ beats 5/11 | 21% |
image to image / editing▁▁·▃▂▃█▃▃▄▁▁ #45 of 62 * · 21% of its score · 1 test
Beats GPT Image 1 Mini, FLUX.1 Kontext [max], Qwen-Image here, though they rank higher overall. Trails GLM-Image, FLUX.2 [klein] 4B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| ServImage task acceptance (human payment decisions, commercial design tasks) | 25.9% ✎ | ▂▃··▂··▃█··▂ beats 5/11 | 21% |
other tests1 not in a task · 16% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Rapidata human-face generation alignment (Elo) | 962.9 ✎ | ▃··▅▃··▃▃··█ beats 2/8 | 16% |
$ where to run it
Through an API
api · fal.ai · $0.060/image · fal.ai ↗ · price sourceUSD per generated image, Ideogram 3 BALANCED setting on fal.ai. TURBO/QUALITY excluded. Product: Ideogram 3 text-to-image. API model: fal-ai/ideogram/v3. Settings: rendering speed=BALANCED, n=1. Price basis: published rate. Currency: USD.
$ how the score was worked out · LMArena leaderboard dataset 62%, ServImage: An Image Generatio… 21%, Rapidata/Face_Generation_Benc… 16%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
| 1 | 62% | |
| 1 | 21% | |
| 1 | 16% | |
| prior · pull toward the middle | 2% | |
| weighted average | = 51.9 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json